Optimizing Routes of Public Transportation Systems by Analyzing the Data of Taxi Rides. - Keven Richly; Ralf Teusner; Alexander Immer; Fabian Windheuser; Lennard Wolf (UrbanGIS@SIGSPATIAL, 2015)

Generative Interest Estimation for Document Recommendations. - Danijar Hafner; Alexander Immer; Willi Raschkowski; Fabian Windheuser (CoRR, 2017)

Efficient learning of smooth probability functions from Bernoulli tests with guarantees. - Paul Rolland; Ali Kavis; Alexander Immer; Adish Singla; Volkan Cevher (ICML, 2019)

Approximate Inference Turns Deep Networks into Gaussian Processes. - Mohammad Emtiyaz Khan; Alexander Immer; Ehsan Abedi; Maciej Korzepa (NeurIPS, 2019)

Sub-Matrix Factorization for Real-Time Vote Prediction. - Alexander Immer; Victor Kristof; Matthias Grossglauser; Patrick Thiran (KDD, 2020)

Continual Deep Learning by Functional Regularisation of Memorable Past. - Pingbo Pan; Siddharth Swaroop; Alexander Immer; Runa Eschenhagen; Richard E. Turner; Mohammad Emtiyaz Khan (NeurIPS, 2020)

Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks. - Alexander Immer (CoRR, 2020)

Improving predictions of Bayesian neural networks via local linearization. - Alexander Immer; Maciej Korzepa; Matthias Bauer 001 (CoRR, 2020)

Improving predictions of Bayesian neural nets via local linearization. - Alexander Immer; Maciej Korzepa; Matthias Bauer 001 (AISTATS, 2021)

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. - Alexander Immer; Matthias Bauer 001; Vincent Fortuin; Gunnar Rätsch; Mohammad Emtiyaz Khan (ICML, 2021)

Laplace Redux - Effortless Bayesian Deep Learning. - Erik A. Daxberger; Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Matthias Bauer 001; Philipp Hennig (NeurIPS, 2021)

Pathologies in priors and inference for Bayesian transformers. - Tristan Cinquin; Alexander Immer; Max Horn; Vincent Fortuin (CoRR, 2021)

Probing as Quantifying the Inductive Bias of Pre-trained Representations. - Alexander Immer; Lucas Torroba Hennigen; Vincent Fortuin; Ryan Cotterell (CoRR, 2021)

Probing as Quantifying Inductive Bias. - Alexander Immer; Lucas Torroba Hennigen; Vincent Fortuin; Ryan Cotterell (ACL (1), 2022)

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. - Alexander Immer; Tycho F. A. van der Ouderaa; Gunnar Rätsch; Vincent Fortuin; Mark van der Wilk (NeurIPS, 2022)

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. - Alexander Immer; Tycho F. A. van der Ouderaa; Mark van der Wilk; Gunnar Rätsch; Bernhard Schölkopf (ICML, 2023)

On the Identifiability and Estimation of Causal Location-Scale Noise Models. - Alexander Immer; Christoph Schultheiss; Julia E. Vogt; Bernhard Schölkopf; Peter Bühlmann; Alexander Marx 001 (ICML, 2023)

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. - Runa Eschenhagen; Alexander Immer; Richard E. Turner; Frank Schneider 001; Philipp Hennig (NeurIPS, 2023)

Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. - Alexander Immer; Emanuele Palumbo; Alexander Marx 001; Julia E. Vogt (NeurIPS, 2023)

Learning Layer-wise Equivariances Automatically using Gradients. - Tycho F. A. van der Ouderaa; Alexander Immer; Mark van der Wilk (NeurIPS, 2023)

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. - Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Vincent Fortuin (CoRR, 2023)

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. - Kouroche Bouchiat; Alexander Immer; Hugo Yèche; Gunnar Rätsch; Vincent Fortuin (CoRR, 2023)

Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. - Alexander Möllers; Alexander Immer; Elvin Isufi; Vincent Fortuin (CoRR, 2023)

Hodge-Aware Contrastive Learning. - Alexander Möllers; Alexander Immer; Vincent Fortuin; Elvin Isufi (ICASSP, 2024)

Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion. - Alexandru Meterez; Amir Joudaki; Francesco Orabona; Alexander Immer; Gunnar Rätsch; Hadi Daneshmand (ICLR, 2024)

Improving Neural Additive Models with Bayesian Principles. - Kouroche Bouchiat; Alexander Immer; Hugo Yèche; Gunnar Rätsch; Vincent Fortuin (ICML, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. - Rayen Dhahri; Alexander Immer; Bertrand Charpentier; Stephan Günnemann; Vincent Fortuin (NeurIPS, 2024)

Probabilistic pathway-based multimodal factor analysis. - Alexander Immer; Stefan G. Stark; Francis Jacob; Ximena Bonilla; Tinu Thomas; André Kahles; Sandra Götze; Emanuela S. Milani; Bernd Wollscheid; Tumor Profiler Consortium; Gunnar Rätsch; Kjong-Van Lehmann (Bioinform., 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

Uncertainty-Penalized Direct Preference Optimization. - Sam Houliston; Alizée Pace; Alexander Immer; Gunnar Rätsch (CoRR, 2024)

Advances in Bayesian Model Selection and Uncertainty Estimation for Deep Learning. - Alexander Immer (2024)

ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish. - Jan-Matthis Lueckmann; Alexander Immer; Alex Bo-Yuan Chen; Peter H. Li; Mariela D. Petkova; Nirmala A. Iyer; Luuk Willem Hesselink; Aparna Dev; Gudrun Ihrke; Woohyun Park; Alyson Petruncio; Aubrey Weigel; et al. (ICLR, 2025)

Influence Functions for Scalable Data Attribution in Diffusion Models. - Bruno Kacper Mlodozeniec; Runa Eschenhagen; Juhan Bae; Alexander Immer; David Krueger 001; Richard E. Turner (ICLR, 2025)

Forecasting Whole-Brain Neuronal Activity from Volumetric Video. - Alexander Immer; Jan-Matthis Lueckmann; Alex Bo-Yuan Chen; Peter H. Li; Mariela D. Petkova; Nirmala A. Iyer; Aparna Dev; Gudrun Ihrke; Woohyun Park; Alyson Petruncio; Aubrey Weigel; Wyatt Korff; et al. (CoRR, 2025)